{"id":14,"date":"2026-10-17T14:44:44","date_gmt":"2026-10-17T14:44:44","guid":{"rendered":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/research\/"},"modified":"2026-10-18T15:19:37","modified_gmt":"2026-10-18T15:19:37","slug":"research","status":"publish","type":"page","link":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<p>My research develops trustworthy AI for cyber-physical systems, especially smart grids and critical energy infrastructure. It is organized around five themes. Each lists representative published work.<\/p>\n<h2>1. Cyber-Physical Security and Intrusion Detection<\/h2>\n<p><strong>Focus:<\/strong> graph neural networks, physics-informed learning, attack graphs, adversarial detection and risk-based security for power systems.<\/p>\n<ul>\n<li><strong>FADEG<\/strong> investigates feature-adaptive graph neural network ensembles for intrusion detection in cyber-physical power systems. <a href=\"https:\/\/doi.org\/10.1109\/OJIA.2026.3675549\">IEEE Open Journal of Industry Applications, 2026<\/a><\/li>\n<li>A constraint-aware physics-informed neural network for intrusion detection in cyber-physical smart grids. <em>Sustainable Energy, Grids and Networks<\/em>, 2026.<\/li>\n<li>Topology-aware hierarchical attack graph optimization for cyber-physical power systems. <a href=\"https:\/\/doi.org\/10.3390\/electronics15132947\">Electronics, 2026<\/a><\/li>\n<li>Maximum entropy inverse reinforcement learning for cyber attack detection in IoT networks. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2026.3701290\">IEEE Access, 2026<\/a><\/li>\n<\/ul>\n<h2>2. Trustworthy AI and Safe Reinforcement Learning<\/h2>\n<p><strong>Focus:<\/strong> robust and resilient decision-making for grid operation under disturbances and adversarial attacks.<\/p>\n<ul>\n<li><strong>SPARQ<\/strong> investigates soft Q-learning for cyber-resilient voltage regulation and autonomous grid operation. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3548517\">IEEE Access, 2025<\/a><\/li>\n<li>Adaptive PPO with multi-armed bandit clipping and meta-control for robust power grid operation under adversarial attacks. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3563419\">IEEE Access, 2025<\/a><\/li>\n<li>A review of deep reinforcement learning for power system stability control. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3337118\">IEEE Access, 2023<\/a><\/li>\n<\/ul>\n<h2>3. Smart-Grid Stability and Intelligent Control<\/h2>\n<p><strong>Focus:<\/strong> transient stability assessment, voltage regulation and learning-based control during grid disturbances.<\/p>\n<ul>\n<li>Fast transient stability assessment using optimized temporal convolutional networks, recipient of the IEEE Industry Applications Society Second Prize Paper Award. <a href=\"https:\/\/doi.org\/10.1109\/OJIA.2024.3426334\">IEEE Open Journal of Industry Applications, 2024<\/a><\/li>\n<li>A review of machine and deep learning for coherent power grid partitioning. <a href=\"https:\/\/doi.org\/10.1109\/OAJPE.2025.3535709\">IEEE Open Access Journal of Power and Energy, 2025<\/a><\/li>\n<\/ul>\n<h2>4. Federated and Distributed Intelligence<\/h2>\n<p><strong>Focus:<\/strong> privacy-preserving learning, concept drift and distributed monitoring of power systems.<\/p>\n<ul>\n<li><strong>FLACON<\/strong> applies deep federated transfer learning to transient stability assessment during symmetrical and asymmetrical grid faults. <a href=\"https:\/\/doi.org\/10.1109\/OJIA.2024.3426281\">IEEE Open Journal of Industry Applications, 2024<\/a><\/li>\n<li>Online transient stability assessment under concept drift using adaptive random forests and federated learning for data streams. <a href=\"https:\/\/doi.org\/10.1109\/JIOT.2025.3583978\">IEEE Internet of Things Journal, 2025<\/a><\/li>\n<\/ul>\n<h2>5. AI for Sustainable Infrastructure and Education<\/h2>\n<p><strong>Focus:<\/strong> resilient energy systems for data centers, distributed energy resources, and AI-powered learning tools.<\/p>\n<ul>\n<li>An efficient framework for multi-DER integration in distribution networks with time-series demand flexibility. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3569152\">IEEE Access, 2025<\/a><\/li>\n<li>An adaptive learning platform for AI education with confidence-aware review and ML coding quests. <em>IEEE ICELIE<\/em>, 2026.<\/li>\n<li>Research software for data-quality assessment: see <a href=\"\/mmassaoudi\/software\/\">Software &amp; Data<\/a>.<\/li>\n<\/ul>\n<h2>Funded project: ReCIPeS-DC<\/h2>\n<h3>Resilient Cyber-Physical Infrastructure for Sustainable Data Centers in Texas<\/h3>\n<table>\n<tbody>\n<tr>\n<th>Role<\/th>\n<td>Lead Principal Investigator<\/td>\n<\/tr>\n<tr>\n<th>Funding<\/th>\n<td>TEES Annual Research Conference (TARC) seed funding, Texas A&amp;M Engineering Experiment Station<\/td>\n<\/tr>\n<tr>\n<th>Award<\/th>\n<td>2026, $5,000<\/td>\n<\/tr>\n<tr>\n<th>Scope<\/th>\n<td>Multi-institutional, AI-enabled cyber-physical resilience framework for data-center-integrated power systems in Texas<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The project aims to build AI-based methods that help power systems serving data centers detect and withstand cyber-physical disruptions. It builds on my work in smart-grid cybersecurity and resilient energy infrastructure. The project is at an early stage, and outputs will be added here as they are published.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>My research develops trustworthy AI for cyber-physical systems, especially smart grids and critical energy infrastructure. It is organized around five themes. Each lists representative published work. 1. Cyber-Physical Security and Intrusion Detection Focus: graph neural networks, physics-informed learning, attack graphs, adversarial detection and risk-based security for power systems. FADEG investigates feature-adaptive graph neural network ensembles &hellip; <a href=\"https:\/\/faculty.tarleton.edu\/mmassaoudi\/research\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Research<\/span><\/a><\/p>\n","protected":false},"author":167,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","footnotes":""},"class_list":["post-14","page","type-page","status-publish","hentry"],"coauthors":[],"author_meta":{"author_link":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/author\/mmassaoudi\/","display_name":"mmassaoudi"},"relative_dates":{"created":"Posted 2 weeks ago","modified":"Updated 2 weeks ago"},"absolute_dates":{"created":"Posted on October 17, 2026","modified":"Updated on October 18, 2026"},"absolute_dates_time":{"created":"Posted on October 17, 2026 2:44 pm","modified":"Updated on October 18, 2026 3:19 pm"},"featured_img_caption":"","featured_img":false,"series_order":"","_links":{"self":[{"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/pages\/14","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/users\/167"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/comments?post=14"}],"version-history":[{"count":1,"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/pages\/14\/revisions"}],"predecessor-version":[{"id":38,"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/pages\/14\/revisions\/38"}],"wp:attachment":[{"href":"https:\/\/faculty.tarleton.edu\/mmassaoudi\/wp-json\/wp\/v2\/media?parent=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}